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import pandas as pd
import numpy as np
from lets_plot import *
from palmerpenguins import load_penguins
LetsPlot.setup_html(isolated_frame=True)import pandas as pd
import numpy as np
from lets_plot import *
from palmerpenguins import load_penguins
LetsPlot.setup_html(isolated_frame=True)# Learn more about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html
# Include and execute your code here
from palmerpenguins import load_penguins
df = load_penguins()Include the tables created from PY4DS: CH2 Data Visualization used to create the above chart (Hint: copy the code from 2.2.1. The penguins data frame and paste each in the cells below)
# Include and execute your code here
penguins = load_penguins()
penguins| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | |
|---|---|---|---|---|---|---|---|---|
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181.0 | 3750.0 | male | 2007 |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186.0 | 3800.0 | female | 2007 |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 195.0 | 3250.0 | female | 2007 |
| 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN | 2007 |
| 4 | Adelie | Torgersen | 36.7 | 19.3 | 193.0 | 3450.0 | female | 2007 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 339 | Chinstrap | Dream | 55.8 | 19.8 | 207.0 | 4000.0 | male | 2009 |
| 340 | Chinstrap | Dream | 43.5 | 18.1 | 202.0 | 3400.0 | female | 2009 |
| 341 | Chinstrap | Dream | 49.6 | 18.2 | 193.0 | 3775.0 | male | 2009 |
| 342 | Chinstrap | Dream | 50.8 | 19.0 | 210.0 | 4100.0 | male | 2009 |
| 343 | Chinstrap | Dream | 50.2 | 18.7 | 198.0 | 3775.0 | female | 2009 |
344 rows × 8 columns
These tables contain all the data on species of penguins that will be displayed in the graphs.
# Include and execute your code here
penguins.head()| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | |
|---|---|---|---|---|---|---|---|---|
| 0 | Adelie | Torgersen | 39.1 | 18.7 | 181.0 | 3750.0 | male | 2007 |
| 1 | Adelie | Torgersen | 39.5 | 17.4 | 186.0 | 3800.0 | female | 2007 |
| 2 | Adelie | Torgersen | 40.3 | 18.0 | 195.0 | 3250.0 | female | 2007 |
| 3 | Adelie | Torgersen | NaN | NaN | NaN | NaN | NaN | 2007 |
| 4 | Adelie | Torgersen | 36.7 | 19.3 | 193.0 | 3450.0 | female | 2007 |
.head() shows the first five rows of data from the first table.
Recreate the example charts from PY4DS: CH2 Data Visualization of the textbook. (Hint: copy the chart code from 2.2.3. Creating a Plot, one for each cell below)
# Include and execute your code here
(
ggplot(data=penguins,
mapping=aes(x="flipper_length_mm", y="body_mass_g"))
+ geom_point()
)I can see the positive correlation between flipper length and body mass after running this code (a longer flipper length seems to indicate a larger body mass).
# Include and execute your code here
(
ggplot(
data=penguins,
mapping=aes(x="flipper_length_mm", y="body_mass_g", color="species"),
)
+ geom_point()
)This color=“species” color codes the points for the three different species of penguins
# Include and execute your code here
(
ggplot(
data=penguins,
mapping=aes(x="flipper_length_mm", y="body_mass_g", color="species"),
)
+ geom_point()
+ geom_smooth(method="lm")
)geom_smooth added lines for each species to better see the positive correlation
# Include and execute your code here
(
ggplot(data=penguins, mapping=aes(x="flipper_length_mm", y="body_mass_g"))
+ geom_point(aes(color="species", shape="species"))
+ geom_smooth(method="lm")
+ labs(
title="Body mass and flipper length",
subtitle="Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
x="Flipper length (mm)",
y="Body mass (g)",
color="Species",
shape="Species",
)
)The labs code adds all the labels for the title, subtitle, x- and y- axis, and species key
# Include and execute your code here
(
ggplot(penguins, aes(x="flipper_length_mm", y="body_mass_g"))
+ geom_point(aes(color="species", shape="species"))
+ facet_wrap(facets="island")
)The facet_wrap divided the data into three charts determined by island so the chart is more readable.